Our hyper-connected digital world is defined by an overabundance of data. From social media to news articles to machine logs, text data is everywhere. The growth in the volume of data created in different formats – text, image or video – and the speed with which they are generated provide interesting new research opportunities that explore social interactions, literature, languages, art, etc. Thinking about the Digital Humanities, at a time when we are witnessing this exponential increase in the volume of digital information available in different languages, inevitably leads us to the exploration of different branches associated with the area of Natural Language Processing (such as information extraction and retrieval, machine translation, automatic analysis of textual content, text summarization, text simplification, text generation, speech recognition and synthesis, among others).

For mining information from very large repositories of text, we need to know what we are looking for and how to analyse it. Our conference is a forum to exchange methods and practical applications to gather, clean, manipulate, and analyze textual data as well as to weave it into compelling, action-inspiring stories using different and new digital forms of multimodal communication.

Topics

We invite linguistic experts, data scientists, IT professionals, developers, and anyone with a keen interest in generating insights from textual data to share ideas and advances on how open sources paradigm and new emerging research text analysis/analytics methods are applied to different fields of humanities and social sciences as well as to discuss current and future challenges. In particular, we encourage the submission of abstracts discussing challenges related to the main stages of data journey presented below. Topics include the following but are open for additional:
1- Getting text data: Where and how do digital humanists find and clean their text data?
Open Access and Open Science
Digital libraries
Data repositories
Language Corpora
Social media
Audio/video data
Web scraping techniques
Text cleaning and parsing techniques
Tools for extracting and cleaning text data
Privacy and/or security requirements

2- Finding inspiration in text data: How do digital humanists find inspiration in their text data?
Document classification
Corpora comparison
Entity recognition
Summarization
Terminology extraction
Text statistics
Topic modeling
Sentiment analysis & Opinion mining
Author profiling
New research methodologies and design
Text simplification

3- Telling a story with text data: Why do digital humanists need to tell stories with their text data?
Visualization as text simplification
Text adaptation
Infographics
Animated videos
Geolocalization
Interactive Dashboards
Instructional Design Research